A computational study of soot formation in opposed-flow diffusion flame interacting with vortices

Prabhu Selvaraj, Hong G. Im

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The flame-vortex interaction enables the study of basic phenomena that control the coupling between combustion and turbulence. Employing a gas phase reaction mechanism considering polycyclic aromatic hydrocarbons (PAH), a two dimensional counterflow ethylene-air flame is simulated. A reduced mechanism with PAH pathways that includes until coronene and method of moments with interpolative closure (MOMIC) has been employed to calculate the soot characteristics. Interaction of sooting flame with a prescribed decaying random velocity field is being investigated. Counterflow nonpremixed flames at low strain rate sooting conditions are considered. Effects of vortices are studied on the flame structures and its sensitivity on the soot formation characteristics. As the vortex rolls up the flame, integrated soot volume fraction is found to be larger for the air-side vortex. A detailed analysis on the flame structure and its influence on the formation of soot were carried out. The results indicate that the larger PAH species contributes to the soot formation in the airside perturbation regimes, whereas the soot formation is dominated by the soot transport in fuel-side perturbation.
Original languageEnglish (US)
Title of host publication55th AIAA Aerospace Sciences Meeting
PublisherAmerican Institute of Aeronautics and Astronautics (AIAA)
ISBN (Print)9781624104473
DOIs
StatePublished - Jan 5 2017

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: The work reported in this paper was sponsored by the King Abdullah University of Science and Technology (KAUST). Simulations made use of computing facilities in KAUST Supercomputing Laboratory. The authors would like to thank Paul G. Arias at the University of Michigan for his contributions in the development of the computational models.

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